## Code that figures grouping results by social class, rather SES quartiles. 
## Last changed: 2025-05-23

sink("C:/Userdata/Shared/Dofiles/DoAnalysis/JanKalle/ReplicationFiles/AnalysisScripts/Output/FigureA16.txt", split=TRUE)

library(PSweight)
library(cobalt)
library(MatchIt)
library(distances)
library(moreparty)
library(party)
library(caret)
library(bonsai)
library(randomForest)
library(tidymodels)
library(tidyverse)
library(rmarkdown)
library(tinytex)
library(tibble)
library(knitr)
library(car)
library(fst)
library(stargazer)
library(pryr)
library(stringr)
library(fst)
library(lubridate)
library(data.table)
library(summarytools)
library(ggplot2)
library(cowplot)
library(ggpubr)
library(haven)
library(broom)
library(fixest)
library(modelsummary)
library(gt)
library(MASS)
library(survey)
library(collapse)
library(readxl)
library(Hmisc)
library(DescTools)
library(ranger)
library(stablelearner)
library(future.apply)
library(lme4)
library(partykit)
library(ggiplot)

  fob90 <- read_fst("D:/SCB_ConPol/Fst/FoB/FoB_90.fst", as.data.table=T)
  fob90 <- fob90[, cID := .N, by = LopNr][cID == 1 & !is.na(LopNr), ][, cID := NULL]
  fob90 <- fob90[, .(LopNr, SEI)]

  fob90[between(SEI, 11, 22), Klass := 0]
  fob90[between(SEI, 33, 36), Klass := 1]
  fob90[between(SEI, 46, 60), Klass := 2]
  fob90[between(SEI, 79, 890), Klass := 3]

# Specify the name of the treatment variable
  tvar <- "T94_90"

# Specify the the birth cohorts to be used for the analysis
  lbyr <- 1942
  ubyr <- 1967

# Specify if only include Swedish citizens 
  citizen <- 1

# Specify the propensity score specification
  psformula <- formula(T94 ~
                       Kon + DAStod_1982 + DAStod_1983 + DAStod_1984 +
                       DAStod_1985 + DAStod_1986 + DAStod_1987 + DAStod_1988 +
                       DAStod_1989 +
                       DAntBarn_91 + Sambo_91 + InvBak  |
                       SSYK3_90 + SNI2_91 + Kommun_91 +
                       rCARB_1982 + rCARB_1983 + rCARB_1984 + rCARB_1985 +
                       rCARB_1986 + rCARB_1987 + rCARB_1988 + rCARB_1989 +
                       pArbInk_1990 + pArbInk_1991 + UtbAr_91 +
                       FodAr + Nom82 + Nom85 + Nom88 + Nom91)

## Estimate two models, one for the complete sample including all observations,
## the second on the restricted sample only including individuals for which
## we can observe all elections bw 82-18 in which they are eligible to run for office. 

  Models <-  vector('list', 10)
  j <- 1
 
for(urval in c(1)) {
for(klass in c(0, 1, 2, 3)) { 
    # Read in the data sets
    NomVald <- read_fst("E:/ProjData/Jan_Kalle/JoPReplications/DB_NomVald.fst", as.data.table=T)
    RTB <- read_fst("E:/ProjData/Jan_Kalle/JoPReplications/DB_RTB.fst", as.data.table=T)
    db <- read_fst("E:/ProjData/Jan_Kalle/JoPReplications/DB_Nom.fst", as.data.table=T)
    
    db[, T94 := get(tvar)]
    mdb <- db[between(FodAr, lbyr, ubyr) & (SvMedb82_18 >= citizen) & (AllaVal >= urval),]
    rm(db)
    gc()
    
    mdb <- fob90[, .(LopNr, SEI, Klass)][mdb, on = "LopNr"]
    mdb <- mdb[Klass == klass]
    
    # Use logit model to estimate propensity scores
    mdb <- mdb[complete.cases(mdb),]
    tm <- proc.time()
    logmod <- feglm(psformula,  
                    data = mdb, family="binomial")
    proc.time()-tm
    summary(logmod)
    
    # Extract predicted probability (propensity scores)
    if(logmod$nobs == logmod$nobs_origin){ 
      mdb[, pT94 := predict(logmod, type = "response")]
    }else{
      mdb[logmod$obs_selection$obsRemoved, pT94 := predict(logmod, type = "response")]
    }
    
    mdb <- mdb[complete.cases(mdb),]
    
    # Now use the Full matching approach of Sävje et al. to obtain ATT and ATE weights.  
    
    tmp <- proc.time()
    set.seed(7892)
    m.out <- matchit(T94~1, data = mdb,
                     distance = mdb$pT94, method = "quick", estimand = "ATT")
    proc.time()-tmp
    m.data1 <- as.data.table(match.data(m.out))[, .(LopNr, weights)]
    setnames(m.data1, "weights", "gm_att")
    
    mdb <- m.data1[, .(LopNr, gm_att)][mdb, on = "LopNr"]
    gc()
    RTB <- mdb[RTB, on = "LopNr"]
    
    rm(mdb)
    gc()
    
    rm(list=setdiff(ls(), c("NomVald", "RTB", "rtbvar", "Models", "j", "tvar", "lbyr", "ubyr", "citizen", "psformula", "urval", "deso91", "fob90", "klass")))
    gc()
    
    
    mdb <- RTB[!is.na(gm_att), 
               .(Kon, Nom82, Nom85, Nom88, Nom91, Sysselsatt_85, DAntBarn_91, Sambo_91, 
                 DStud_91, DForLed_91, DSjukRe_91, DSocBidrFam_91, DBostBidrFam_91,
                 UtbAr_91, FodAr, InvBak, isei, Yrke80_90, SNI2_90, Kommun_91, fp_LoneInk_85, fp_LoneInk_90, fp_LoneInk_91,
                 gm_att, Nom, LopNr, T94, Ar, pT94)]
    
    gc()
    
    mdb[, Nom := Nom*100]
    mdb[, cID := .N, by=.(LopNr, Ar)]
    #mdb <- mdb[(Ar-FodAr)<70, ]
    
    rm(RTB)
    gc()
    
    
    Models[[j]] <- feols(Nom~T94+i(Ar, T94, ref=1991) | Ar, cluster ="LopNr", 
                         weights = mdb[, gm_att], 
                         mdb[])
    
    
    print(qsu(mdb[Ar==1991, .(Nom)]))
    print(summary(Models[[j]]))
    print(uniqueN(mdb[T94==0, LopNr]))
    print(uniqueN(mdb[T94==1, LopNr]))
    print(uniqueN(mdb[, LopNr]))
    print(dim(mdb))
    print(qsu(mdb$cID))
    j <- j+1
}  
}

  gi <- ggiplot(Models[[1]])
  gi

  valar <- c(1982, 1985, 1988, 1991, 1994, 1998, 2002, 2006, 2010, 2014, 2018, 2022)
  options(scipen=999)

  for(i in 1:4) {
    
    ses <- ""
    if(i %in% c(1)) ses <- paste0("Blue Collar")
    if(i %in% c(2)) ses <- paste0("Lower White Collar")
    if(i %in% c(3)) ses <- paste0("Upper White Collar")
    if(i %in% c(4)) ses <- paste0("Self Employed")
    if(i %in% c(5)) ses <- paste0("Blue Collar")
    if(i %in% c(6)) ses <- paste0("Lower White Collar")
    if(i %in% c(7)) ses <- paste0("Upper White Collar")
    if(i %in% c(8)) ses <- paste0("Self Employed")
    
    
    gi <- ggiplot(Models[[i]]) 
    gp <- ggplot(gi$data, aes(x, estimate)) +
      geom_point() + 
      geom_hline(yintercept=0, linetype="longdash") +
      geom_errorbar(aes(ymin=ci_low,ymax=ci_high), width=0.4, size=.6) +
      geom_line(linetype="dotted") +
      scale_x_continuous(breaks = valar,  labels = valar, limits = c(1981, 2023)) +
      scale_y_continuous(limits = c(-.4, 1.4), breaks = round(seq(-.4, 1.4, by = .2), digits=1)) +
      xlab("Election Year") +
      ylab("Effect on Candidacy") +
      ggtitle(ses) +
      theme_classic(base_size = 14) +
      theme(legend.title=element_blank()) +
      theme(legend.position = "bottom", panel.grid.major.y = element_line(color = "grey95"))
    
    gp
    #ggsave(paste0("C:/Userdata/Shared/Dofiles/DoAnalysis/JanKalle/ReplicationFiles/AnalysisScripts/Figures/Fig_", j, "_", i, "klass.pdf"), width = 10, height =7.5, units = "in") 
    
    assign(paste0("gp", i), gp)
  }  

  ggarrange(gp1, gp2, gp3, gp4)
  ggsave("C:/Userdata/Shared/Dofiles/DoAnalysis/JanKalle/ReplicationFiles/AnalysisScripts/Figures/FigureA16.pdf", width = 15, height =10, units = "in") 

  modelsummary(Models[c(1, 2, 3, 4)], output = "C:/Userdata/Shared/Dofiles/DoAnalysis/JanKalle/ReplicationFiles/AnalysisScripts/Tables/TableFigA16.tex")
  
  sink()
  